{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TextCNN+bn 模型。dropout=0.8 最后还是过拟合了，效果并没有比 dropout=0.5 好。\n",
    "\n",
    "参考代码： \n",
    "\n",
    "https://github.com/brightmart/text_classification/blob/master/a02_TextCNN/p7_TextCNN_model.py \n",
    "\n",
    "https://github.com/Qinbf/Tensorflow/blob/master/Tensorflow%E5%9F%BA%E7%A1%80%E4%BD%BF%E7%94%A8%E4%B8%8E%E6%96%87%E6%9C%AC%E5%88%86%E7%B1%BB%E5%BA%94%E7%94%A8/%E7%A8%8B%E5%BA%8F/cnn.ipynb\n",
    "\n",
    "- 每个模型保存到对应的模型位置\n",
    "- 每个模型生成一个 scores 分数矩阵用于后面进行模型融合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting ...\n",
      "removed the existing summary files.\n",
      "Prepared, costed time 0.768948 s.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "from gensim.models import KeyedVectors\n",
    "import pickle\n",
    "import os\n",
    "import sys\n",
    "import shutil\n",
    "import time\n",
    "\n",
    "\n",
    "time0 = time.time()\n",
    "print('Starting ...')\n",
    "model_name = 'textcnn-title-256-len50'                    # 模型名称\n",
    "W_embedding = np.load('../data/W_embedding.npy')            # 导入预训练好的词向量\n",
    "model_path = '../ckpt/' + model_name + '/'                  # 模型保存位置\n",
    "summary_path = '../summary/' + model_name + '/'             # summary 位置\n",
    "result_path = '../result/' + model_name + '.csv'            # result.csv 位置\n",
    "scores_path = '../scores/' + model_name + '.npy'            # scores.npy 位置\n",
    "\n",
    "if not os.path.exists(model_path):\n",
    "    os.makedirs(model_path)         \n",
    "model_path = model_path + 'model.ckpt'\n",
    "if os.path.exists(summary_path):   # 删除原来的 summary 文件，避免重合\n",
    "    print('removed the existing summary files.')\n",
    "    shutil.rmtree(summary_path)\n",
    "os.makedirs(summary_path)          # 然后再次创建\n",
    "    \n",
    "# ##################### config ######################\n",
    "n_step = max_len = 50                   # 句子长度\n",
    "input_size = embedding_size = 256       # 字向量长度\n",
    "n_class = 1999                          # 类别总数\n",
    "filter_sizes = [3,4,5]                  # 卷积核大小\n",
    "n_filter = 256                          # 每种卷积核的个数\n",
    "n_filter_total = n_filter * len(filter_sizes)\n",
    "global_step = 0\n",
    "valid_num = 300000\n",
    "seed_num = 13\n",
    "print('Prepared, costed time %g s.' % (time.time() - time0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Building model ...\n",
      "Finished creating the TextCNN model.\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "config = tf.ConfigProto()\n",
    "config.gpu_options.allow_growth = True\n",
    "sess = tf.Session(config=config)\n",
    "from tensorflow.contrib import rnn\n",
    "import tensorflow.contrib.layers as layers\n",
    "\n",
    "'''\n",
    "双端 GRU，知乎问题多标签分类。\n",
    "'''\n",
    "print('Building model ...')\n",
    "lr = tf.placeholder(tf.float32)\n",
    "keep_prob = tf.placeholder(tf.float32, [])\n",
    "batch_size = tf.placeholder(tf.int32, [])  # 注意类型必须为 tf.int32\n",
    "tst = tf.placeholder(tf.bool)\n",
    "n_updates = tf.placeholder(tf.int32)      # training iteration,传入 bn 层\n",
    "update_emas = list()   # BN 层中所有的更新操作\n",
    "\n",
    "\n",
    "def weight_variable(shape, name):\n",
    "    \"\"\"Create a weight variable with appropriate initialization.\"\"\"\n",
    "    initial = tf.truncated_normal(shape, stddev=0.1)\n",
    "    return tf.Variable(initial, name=name)\n",
    "\n",
    "def bias_variable(shape, name):\n",
    "    \"\"\"Create a bias variable with appropriate initialization.\"\"\"\n",
    "    initial = tf.constant(0.1, shape=shape)\n",
    "    return tf.Variable(initial, name=name)\n",
    "\n",
    "def batchnorm(Ylogits, is_test, num_updates, offset, convolutional=False):\n",
    "    \"\"\"batchnormalization.\n",
    "    Args:\n",
    "        Ylogits: 1D向量或者是3D的卷积结果。\n",
    "        num_updates: 迭代的global_step\n",
    "        offset：表示beta，全局均值；在 RELU 激活中一般初始化为 0.1。\n",
    "        scale：表示lambda，全局方差；在 sigmoid 激活中需要，这 RELU 激活中作用不大。\n",
    "        m: 表示batch均值；v:表示batch方差。\n",
    "        bnepsilon：一个很小的浮点数，防止除以 0.\n",
    "    Returns:\n",
    "        Ybn: 和 Ylogits 的维度一样，就是经过 Batch Normalization 处理的结果。\n",
    "        update_moving_everages：更新mean和variance，主要是给最后的 test 使用。\n",
    "    \"\"\"\n",
    "    exp_moving_avg = tf.train.ExponentialMovingAverage(0.999, num_updates) # adding the iteration prevents from averaging across non-existing iterations\n",
    "    bnepsilon = 1e-5\n",
    "    if convolutional:\n",
    "        mean, variance = tf.nn.moments(Ylogits, [0, 1, 2])\n",
    "    else:\n",
    "        mean, variance = tf.nn.moments(Ylogits, [0])\n",
    "    update_moving_everages = exp_moving_avg.apply([mean, variance])\n",
    "    m = tf.cond(is_test, lambda: exp_moving_avg.average(mean), lambda: mean)\n",
    "    v = tf.cond(is_test, lambda: exp_moving_avg.average(variance), lambda: variance)\n",
    "    Ybn = tf.nn.batch_normalization(Ylogits, m, v, offset, None, bnepsilon)\n",
    "    return Ybn, update_moving_everages\n",
    "\n",
    "\n",
    "with tf.name_scope('Inputs'):\n",
    "    X_inputs = tf.placeholder(tf.int64, [None, n_step], name='X_input')\n",
    "    y_inputs = tf.placeholder(tf.float32, [None, n_class], name='y_input')    \n",
    "\n",
    "with tf.name_scope('embedding_layer'):\n",
    "    embedding = tf.get_variable(name=\"W_embedding\", shape=W_embedding.shape, \n",
    "                        initializer=tf.constant_initializer(W_embedding), trainable=True)   # fine-tune\n",
    "\n",
    "def textcnn(X_inputs):\n",
    "    \"\"\"build the TextCNN network. Return the h_drop\"\"\"\n",
    "    # X_inputs.shape = [batchsize, n_step]  ->  inputs.shape = [batchsize, n_step, embedding_size]\n",
    "    inputs = tf.nn.embedding_lookup(embedding, X_inputs)  \n",
    "    inputs = tf.expand_dims(inputs, -1)\n",
    "    pooled_outputs = list()\n",
    "    for i, filter_size in enumerate(filter_sizes):\n",
    "        with tf.name_scope(\"conv-maxpool-%s\" % filter_size):\n",
    "            # Convolution Layer\n",
    "            filter_shape = [filter_size, embedding_size, 1, n_filter]\n",
    "            W_filter = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W_filter\")\n",
    "            tf.summary.histogram('W_filter', W_filter)\n",
    "#             b_filter = tf.Variable(tf.constant(0.1, shape=[n_filter]), name=\"b_filter\")   # 使用 offset，就不用 biases了\n",
    "#             tf.summary.histogram('b_filter', b_filter)\n",
    "            beta = tf.Variable(tf.constant(0.1, tf.float32, shape=[n_filter], name=\"beta\"))\n",
    "            tf.summary.histogram('beta', beta)\n",
    "            conv = tf.nn.conv2d(inputs, W_filter, strides=[1, 1, 1, 1], padding=\"VALID\", name=\"conv\")\n",
    "            conv_bn, update_ema = batchnorm(conv, tst, n_updates, beta, convolutional=True)    # 在激活层前面加 BN\n",
    "            # Apply nonlinearity\n",
    "            # batch norm scaling is not useful with relus\n",
    "            # batch norm offsets are used instead of biases,使用 BN 层的 offset，不要 biases\n",
    "            h = tf.nn.relu(conv_bn, name=\"relu\")\n",
    "            # Maxpooling over the outputs\n",
    "            pooled = tf.nn.max_pool(h,ksize=[1, n_step - filter_size + 1, 1, 1],\n",
    "                strides=[1, 1, 1, 1],padding='VALID',name=\"pool\")\n",
    "            pooled_outputs.append(pooled)\n",
    "            update_emas.append(update_ema)\n",
    "    h_pool = tf.concat(pooled_outputs, 3)\n",
    "    h_pool_flat = tf.reshape(h_pool, [-1, n_filter_total])\n",
    "    with tf.name_scope('dropout'):\n",
    "        h_drop = tf.nn.dropout(h_pool_flat, keep_prob)  # h_drop.shape = [-1, num_filters_total]\n",
    "    return h_drop     \n",
    "\n",
    "with tf.name_scope('textcnn'):\n",
    "    h_drop = textcnn(X_inputs)\n",
    "    \n",
    "with tf.name_scope('out_layer'):\n",
    "    W_out = weight_variable([n_filter_total, n_class], name='Weight_out') \n",
    "    tf.summary.histogram('Weight_out', W_out)\n",
    "    b_out = bias_variable([n_class], name='bias_out') \n",
    "    tf.summary.histogram('bias_out', b_out)\n",
    "    y_pred = tf.nn.xw_plus_b(h_drop, W_out, b_out, name='y_pred')  #每个类别的分数 scores\n",
    "with tf.name_scope('cost'):\n",
    "    cost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=y_pred, labels=y_inputs))\n",
    "    tf.summary.scalar('cost', cost)\n",
    "\n",
    "# 优化器\n",
    "with tf.name_scope('AdamOptimizer'):\n",
    "    optimizer = tf.train.AdamOptimizer(learning_rate=lr)\n",
    "    train_op = optimizer.minimize(cost)\n",
    "    update_op = tf.group(*update_emas)   # 更新 BN 参数\n",
    "\n",
    "# summary\n",
    "merged = tf.summary.merge_all() # summary\n",
    "train_writer = tf.summary.FileWriter(summary_path + 'train', sess.graph)\n",
    "test_writer = tf.summary.FileWriter(summary_path + 'test')\n",
    "print 'Finished creating the TextCNN model.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loading data ...\n"
     ]
    }
   ],
   "source": [
    "sys.path.append('..')\n",
    "from data_helpers import BatchGenerator\n",
    "from data_helpers import to_categorical\n",
    "from evaluator import score_eval\n",
    "\n",
    "print 'loading data ...'\n",
    "time0 = time.time()\n",
    "save_path = '../data/'\n",
    "X = np.load(save_path+'X_tr_title_50.npy')\n",
    "y = np.load(save_path+'y_tr.npy')\n",
    "# 划分验证集\n",
    "sample_num = X.shape[0]\n",
    "np.random.seed(seed_num)\n",
    "new_index = np.random.permutation(sample_num)\n",
    "X = X[new_index]\n",
    "y = y[new_index]\n",
    "X_valid = X[:valid_num]\n",
    "# y_valid = y[:valid_num]\n",
    "# X_train = X[valid_num:]\n",
    "# y_train = y[valid_num:]\n",
    "# print('train_num=%d, valid_num=%d' % (X_train.shape[0], X_valid.shape[0]))\n",
    "# # 构建数据生成器\n",
    "# data_train = BatchGenerator(X_train, y_train, shuffle=True)\n",
    "# data_valid = BatchGenerator(X_valid, y_valid, shuffle=False)\n",
    "# print('X_train.shape=', X_train.shape, 'y_train.shape=', y_train.shape)\n",
    "# print('X_valid.shape=', X_valid.shape, 'y_valid.shape=', y_valid.shape)\n",
    "\n",
    "# # valid 数据及 验证集计算\n",
    "# marked_labels_list = data_valid.y.tolist() # 所有的标注结果\n",
    "# valid_data_size = data_valid.y.shape[0]\n",
    "# def valid_epoch():\n",
    "#     \"\"\"Testing or valid.\"\"\"\n",
    "#     data_valid._index_in_epoch = 0  # 先指向第一个值\n",
    "#     _batch_size = 4000\n",
    "#     fetches = [merged, cost, y_pred]   \n",
    "#     batch_num = int(valid_data_size / _batch_size)\n",
    "#     start_time = time.time()\n",
    "#     _costs = 0.0\n",
    "#     predict_labels_list = list()  # 所有的预测结果\n",
    "#     for i in xrange(batch_num):\n",
    "#         X_batch, y_batch = data_valid.next_batch(_batch_size)\n",
    "#         y_batch = to_categorical(y_batch)\n",
    "#         feed_dict = {X_inputs:X_batch, y_inputs:y_batch, lr:1e-5, batch_size:_batch_size, keep_prob:1.0, tst:True, n_updates:global_step}\n",
    "#         summary, _cost, predict_labels = sess.run(fetches, feed_dict)\n",
    "#         _costs += _cost    \n",
    "#         predict_labels = map(lambda label: label.argsort()[-1:-6:-1], predict_labels) # 取最大的5个下标\n",
    "#         predict_labels_list.extend(predict_labels)\n",
    "#     test_writer.add_summary(summary, global_step)\n",
    "#     predict_label_and_marked_label_list = zip(predict_labels_list, marked_labels_list)\n",
    "#     precision, recall, f1 = score_eval(predict_label_and_marked_label_list)\n",
    "#     mean_cost = _costs / batch_num\n",
    "#     return mean_cost, precision, recall, f1\n",
    "\n",
    "# print('Finised loading data, time %g s' % (time.time() - time0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH 1， lr=0.0015\n",
      "global_step=6000: valid cost=0.00674229; p=1.0896, r=0.444565, f1=0.31574 \n",
      "training cost=0.00804923; valid cost=0.00737677; p=1.165, r=0.475192, f1=0.337521; speed=1398.45 s/epoch\n",
      "('the save path is ', '../ckpt/textcnn-title-256-len50/model.ckpt-1')\n",
      "EPOCH 2， lr=0.0015\n",
      "global_step=16546: valid cost=0.00528478; p=1.25022, r=0.509573, f1=0.362019 \n",
      "training cost=0.00497216; valid cost=0.00482294; p=1.28017, r=0.521182, f1=0.370389; speed=1339.08 s/epoch\n",
      "('the save path is ', '../ckpt/textcnn-title-256-len50/model.ckpt-2')\n",
      "EPOCH 3， lr=0.000975\n",
      "global_step=27092: valid cost=0.00452673; p=1.30917, r=0.533035, f1=0.378803 \n",
      "training cost=0.00464207; valid cost=0.00471893; p=1.30846, r=0.532823, f1=0.378636; speed=1370.89 s/epoch\n",
      "('the save path is ', '../ckpt/textcnn-title-256-len50/model.ckpt-3')\n",
      "EPOCH 4， lr=0.00063375\n",
      "global_step=37638: valid cost=0.00439226; p=1.33039, r=0.541538, f1=0.384874 \n",
      "training cost=0.00446273; valid cost=0.00438631; p=1.33423, r=0.543007, f1=0.385938; speed=1446.84 s/epoch\n",
      "('the save path is ', '../ckpt/textcnn-title-256-len50/model.ckpt-4')\n",
      "EPOCH 5， lr=0.000411938\n",
      "global_step=48184: valid cost=0.00438008; p=1.33605, r=0.543751, f1=0.386466 \n",
      "training cost=0.00434585; valid cost=0.00434561; p=1.34236, r=0.54607, f1=0.388165; speed=1289.79 s/epoch\n",
      "('the save path is ', '../ckpt/textcnn-title-256-len50/model.ckpt-5')\n",
      "EPOCH 6， lr=0.000267759\n",
      "global_step=58730: valid cost=0.0043351; p=1.34451, r=0.546979, f1=0.388804 \n",
      "training cost=0.00426781; valid cost=0.00432753; p=1.34664, r=0.547948, f1=0.389472; speed=1285.77 s/epoch\n",
      "('the save path is ', '../ckpt/textcnn-title-256-len50/model.ckpt-6')\n",
      "EPOCH 7， lr=0.000174044\n",
      "global_step=69276: valid cost=0.00431899; p=1.34826, r=0.548498, f1=0.389885 \n",
      "training cost=0.00421408; valid cost=0.00435055; p=1.34545, r=0.547482, f1=0.389137; speed=1275.59 s/epoch\n",
      "('the save path is ', '../ckpt/textcnn-title-256-len50/model.ckpt-7')\n",
      "EPOCH 8， lr=0.000113128\n",
      "global_step=79822: valid cost=0.00431891; p=1.34933, r=0.548655, f1=0.390054 \n",
      "training cost=0.00417783; valid cost=0.00432375; p=1.34926, r=0.549019, f1=0.390232; speed=1284.33 s/epoch\n",
      "('the save path is ', '../ckpt/textcnn-title-256-len50/model.ckpt-8')\n"
     ]
    }
   ],
   "source": [
    "tr_batch_size = 256      \n",
    "_lr = 1.5e-3 \n",
    "max_epoch = 2            # 超多 max_epoch 以后开始降低 lr\n",
    "decay = 0.65             # 每个 epoch lr降低的系数\n",
    "valid_step = 6000        # 每 valid_step 就进行一次 valid 运算\n",
    "max_max_epoch = 8       # 最多迭代的次数\n",
    "save_epoch = 1           # 每迭代 save_epoch 次保存一次模型\n",
    "tr_batch_num = int(data_train.y.shape[0] / tr_batch_size)  # 每个 epoch 中包含的 batch 数\n",
    "saver = tf.train.Saver(max_to_keep=12)           # 最多保存的模型数量\n",
    "sess.run(tf.global_variables_initializer())\n",
    "\n",
    "for epoch in xrange(max_max_epoch):\n",
    "    fetches = [merged, cost, train_op, update_op]\n",
    "    if (epoch >= max_epoch):\n",
    "        _lr = _lr * decay\n",
    "    print 'EPOCH %d， lr=%g' % (epoch+1, _lr)\n",
    "    time0 = time.time()\n",
    "    start_time = time.time()\n",
    "    _costs = 0.0\n",
    "    show_costs = 0.0\n",
    "    for batch in xrange(tr_batch_num): \n",
    "        global_step += 1\n",
    "        if (batch+1) % valid_step == 0:    # 进行 valid 计算\n",
    "            valid_cost, precision, recall, f1 = valid_epoch()\n",
    "            print('global_step=%d: valid cost=%g; p=%g, r=%g, f1=%g ' % (\n",
    "                    global_step, valid_cost, precision, recall, f1))\n",
    "        X_batch, y_batch = data_train.next_batch(tr_batch_size)\n",
    "        y_batch = to_categorical(y_batch)\n",
    "        feed_dict = {X_inputs:X_batch, y_inputs:y_batch, lr:_lr, batch_size:tr_batch_size, keep_prob:0.5, tst:False, n_updates:global_step}\n",
    "        summary, _cost, _, _ = sess.run(fetches, feed_dict) # the cost is the mean cost of one batch\n",
    "        _costs += _cost\n",
    "        if global_step % 100:\n",
    "            train_writer.add_summary(summary, global_step)\n",
    "    valid_cost, precision, recall, f1 = valid_epoch()  # # 每个 epoch 进行一次验证 valid\n",
    "    mean_cost = _costs / tr_batch_num\n",
    "    print('training cost=%g; valid cost=%g; p=%g, r=%g, f1=%g; speed=%g s/epoch' % (\n",
    "        mean_cost, valid_cost, precision, recall, f1, time.time()-time0) )\n",
    "    if (epoch + 1) % save_epoch == 0:  # 每 2 个 epoch 保存一次模型\n",
    "        save_path = saver.save(sess, model_path, global_step=(epoch+1))\n",
    "        print('the save path is ', save_path) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/jpeg": "/9j/4AAQSkZJRgABAQEAYABgAAD/2wBDAAgGBgcGBQgHBwcJCQgKDBQNDAsLDBkSEw8UHRofHh0a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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('../figs/cnn-title-256.jpg')   # 结果保存了时间步。。。看个屁"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Bi-LSTM 模型<br/>\n",
    "**batch_size=256，迭代12个epoch，基本收敛。结果： 验证集 f1=0.38618； 提交结果 0.3873186**\n",
    "- Bi-GRU 模型<br/>\n",
    "**batch_size=256，迭代15个epoch。大概在 13 个 epoch 就收敛了。结果： 验证集 f1=0.390534； 提交结果 0.39198**\n",
    "- attention-Bi-GRU 模型<br/>\n",
    "**batch_size=256，迭代18个epoch。在16个epoch收敛。结果：验证集 f1=f1=0.391734 ；提交结果 0.39310**\n",
    "\n",
    "\n",
    "- textcnn-256 lr=1e-3, decay=0.65, dropout=0.5, 迭代6次基本收敛， f1=0.388\n",
    "- textcnn-256-bn lr=1.5E-3，decay=0.65, dropout=0.5, 6次基本收敛，8次好一点， f1=0.389"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 若没有收敛，继续迭代"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EPOCH 6， lr=1.34627e-05\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Cannot interpret feed_dict key as Tensor: Can not convert a float into a Tensor.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-39c8fc0d14d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0my_batch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_categorical\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mfeed_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mX_inputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mX_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_inputs\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0my_batch\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0m_lr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtr_batch_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeep_prob\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0.8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtst\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_updates\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mglobal_step\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0msummary\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_cost\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfetches\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# the cost is the mean cost of one batch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m         \u001b[0m_costs\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0m_cost\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mglobal_step\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/common/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m       result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 789\u001b[0;31m                          run_metadata_ptr)\n\u001b[0m\u001b[1;32m    790\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m         \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/common/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m    943\u001b[0m           \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m             raise TypeError('Cannot interpret feed_dict key as Tensor: '\n\u001b[0;32m--> 945\u001b[0;31m                             + e.args[0])\n\u001b[0m\u001b[1;32m    946\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    947\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msubfeed_val\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Cannot interpret feed_dict key as Tensor: Can not convert a float into a Tensor."
     ]
    }
   ],
   "source": [
    "_lr=2e-4\n",
    "global_step=50185\n",
    "add_epoch = 2   # 增加迭代的次数\n",
    "for epoch in xrange(max_max_epoch,max_max_epoch+add_epoch):\n",
    "    fetches = [merged, cost, train_op]\n",
    "    _lr = _lr * decay\n",
    "    print 'EPOCH %d， lr=%g' % (epoch+1, _lr)\n",
    "    time0 = time.time()\n",
    "    start_time = time.time()\n",
    "    _costs = 0.0\n",
    "    show_costs = 0.0\n",
    "    for batch in xrange(tr_batch_num): \n",
    "        global_step += 1\n",
    "        if (batch+1) % valid_step == 0:    # 进行 valid 计算\n",
    "            valid_cost, precision, recall, f1 = valid_epoch()\n",
    "            print('global_step=%d: valid cost=%g; p=%g, r=%g, f1=%g ' % (\n",
    "                    global_step+1, valid_cost, precision, recall, f1))\n",
    "        X_batch, y_batch = data_train.next_batch(tr_batch_size)\n",
    "        y_batch = to_categorical(y_batch)\n",
    "        feed_dict = {X_inputs:X_batch, y_inputs:y_batch, lr:_lr, batch_size:tr_batch_size, keep_prob:0.5, tst:False, n_updates:global_step}\n",
    "        summary, _cost, _ = sess.run(fetches, feed_dict) # the cost is the mean cost of one batch\n",
    "        _costs += _cost\n",
    "        if global_step % 100:\n",
    "            train_writer.add_summary(summary, global_step)\n",
    "    valid_cost, precision, recall, f1 = valid_epoch()  # # 每个 epoch 进行一次验证 valid\n",
    "    mean_cost = _costs / tr_batch_num\n",
    "    print('training cost=%g; valid cost=%g; p=%g, r=%g, f1=%g; speed=%g s/epoch' % (\n",
    "        mean_cost, valid_cost, precision, recall, f1, time.time()-time0) )\n",
    "    if (epoch + 1) % save_epoch == 0:  # 每 2 个 epoch 保存一次模型\n",
    "        save_path = saver.save(sess, model_path, global_step=(epoch+1))\n",
    "        print('the save path is ', save_path) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ../ckpt/textcnn-title-256/model.ckpt-5\n",
      "Finished loading model.\n"
     ]
    }
   ],
   "source": [
    "# 导入保存好的模型\n",
    "saver = tf.train.Saver()\n",
    "best_model_path = model_path + '-' + str(5)  # 导入最优模型\n",
    "saver.restore(sess, best_model_path)\n",
    "print('Finished loading model.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 本地预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "begin ...\n",
      "INFO:tensorflow:Restoring parameters from ../ckpt/textcnn-title-256-len50/model.ckpt-8\n",
      "Finished loading model.\n"
     ]
    }
   ],
   "source": [
    "sys.path.append('..')\n",
    "from evaluator import score_eval\n",
    "\n",
    "print('begin ...')\n",
    "# 保存 本地测试的标注数据\n",
    "marked_labels_list = np.load('../data/marked_labels_list.npy')\n",
    "local_scores_path = '../local_scores/' + model_name + '.npy'\n",
    "\n",
    "# 导入保存好的模型\n",
    "saver = tf.train.Saver()\n",
    "best_model_path = model_path + '-' + str(8)  # 导入最优模型\n",
    "saver.restore(sess, best_model_path)\n",
    "print('Finished loading model.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "local predicting ...\n",
      "local valid p=1.34741, r=0.547891, f1=0.389508; speed=20.9035 s/epoch\n",
      "Writed the scores into ../local_scores/textcnn-title-256-len50.npy, time 26.8274 s\n"
     ]
    }
   ],
   "source": [
    "# 导入测试数据\n",
    "def local_predict(scores_path=local_scores_path):\n",
    "    \"\"\"预测  valid 结果，并保存预测概率 到  scores.csv 文件中。\"\"\"\n",
    "    print('local predicting ...')\n",
    "    time0 = time.time()\n",
    "    X_te = X_valid[:100000, :]\n",
    "    n_sample = X_te.shape[0]  # 测试样本数量\n",
    "    _batch_size = 100\n",
    "    fetches = [y_pred]   \n",
    "    predict_labels_list = list()  # 所有的预测结果\n",
    "    predict_scores = list()\n",
    "    for i in xrange(0, n_sample, _batch_size):\n",
    "        start = i\n",
    "        end = start+_batch_size\n",
    "        if end > n_sample:\n",
    "            end = n_sample\n",
    "            _batch_size = end - start\n",
    "        X_batch = X_te[start:end]\n",
    "        feed_dict = {X_inputs:X_batch, lr:1e-5,\n",
    "                     batch_size:_batch_size, keep_prob:1.0, tst:True, n_updates:global_step}\n",
    "        predict_labels = sess.run(fetches, feed_dict)[0]\n",
    "        predict_scores.append(predict_labels)\n",
    "        predict_labels = map(lambda label: label.argsort()[-1:-6:-1], predict_labels) # 取最大的5个下标\n",
    "        predict_labels_list.extend(predict_labels)\n",
    "    predict_scores = np.asarray(predict_scores)\n",
    "    predict_label_and_marked_label_list = zip(predict_labels_list, marked_labels_list)\n",
    "    precision, recall, f1 = score_eval(predict_label_and_marked_label_list)\n",
    "    print('local valid p=%g, r=%g, f1=%g; speed=%g s/epoch' % ( precision, recall, f1, time.time()-time0) )\n",
    "    np.save(local_scores_path, predict_scores)\n",
    "    print('Writed the scores into %s, time %g s' % (local_scores_path, time.time()-time0))\n",
    "    \n",
    "local_predict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 对测试数据进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 导入保存好的模型\n",
    "# saver = tf.train.Saver()\n",
    "# best_model_path = model_path + '-' + str(8)  # 导入最优模型\n",
    "# saver.restore(sess, best_model_path)\n",
    "# print('Finished loading model.')\n",
    "\n",
    "# 导入测试数据\n",
    "def predict():\n",
    "    \"\"\"预测测试集结果，并保存到  result.csv 文件中。\"\"\"\n",
    "    X_te = np.load('../data/X_te_title_50.npy')\n",
    "    n_sample = X_te.shape[0]  # 测试样本数量\n",
    "    _batch_size = 1000\n",
    "    fetches = [y_pred]   \n",
    "    predict_labels_list = list()  # 所有的预测结果\n",
    "    predict_scores = list()\n",
    "    for i in xrange(0, n_sample, _batch_size):\n",
    "        start = i\n",
    "        end = start+_batch_size\n",
    "        if end > n_sample:\n",
    "            end = n_sample\n",
    "            _batch_size = end - start\n",
    "        X_batch = X_te[start:end]\n",
    "        feed_dict = {X_inputs:X_batch, lr:1e-5, batch_size:_batch_size, keep_prob:1.0, tst:True, n_updates:global_step}\n",
    "        predict_labels = sess.run(fetches, feed_dict)[0]\n",
    "        predict_scores.append(predict_labels)\n",
    "        predict_labels = map(lambda label: label.argsort()[-1:-6:-1], predict_labels) # 取最大的5个下标\n",
    "        predict_labels_list.extend(predict_labels)\n",
    "    predict_scores = np.asarray(predict_scores)\n",
    "    return predict_labels_list, np.vstack(predict_scores)\n",
    "\n",
    "def write_result(predict_labels_list, result_path):\n",
    "    \"\"\"把结果写到 result.csv 中\"\"\"\n",
    "    eval_question = np.load('../data/eval_question.npy')\n",
    "    with open('../data/sr_topic2id.pkl', 'rb') as inp:\n",
    "        sr_topic2id = pickle.load(inp)\n",
    "        sr_id2topic = pickle.load(inp)\n",
    "    pred_labels = np.asarray(predict_labels_list).reshape([-1])\n",
    "    pred_topics = sr_id2topic[pred_labels].values.reshape([-1, 5])   # 转为 topic\n",
    "    df_result = pd.DataFrame({'question':eval_question, 'tid0': pred_topics[:,0], 'tid1':pred_topics[:, 1],\n",
    "                         'tid2': pred_topics[:,2], 'tid3':pred_topics[:,3],'tid4': pred_topics[:,4]})\n",
    "    df_result.to_csv(result_path, index=False, header=False)\n",
    "    print('Finished writing the result')\n",
    "    return df_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 37.8 s, sys: 1min 11s, total: 1min 49s\n",
      "Wall time: 1min 47s\n",
      "Finished writing the result\n",
      "('len(df_result)=', 217360)\n",
      "Saving the predict_scores into ../scores/textcnn-title-256-len50.npy\n",
      "('predict_scores.shape=', (217360, 1999))\n",
      "Finished saving the result!\n"
     ]
    }
   ],
   "source": [
    "%time predict_labels_list,predict_scores = predict()\n",
    "df_result = write_result(predict_labels_list, result_path=result_path) \n",
    "print('len(df_result)=',len(df_result))  # 结果应该为 217360\n",
    "print('Saving the predict_scores into %s' % scores_path)\n",
    "print('predict_scores.shape=',predict_scores.shape)\n",
    "np.save(scores_path, predict_scores)\n",
    "print('Finished saving the result!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>tid0</th>\n",
       "      <th>tid1</th>\n",
       "      <th>tid2</th>\n",
       "      <th>tid3</th>\n",
       "      <th>tid4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6215603645409872328</td>\n",
       "      <td>4610596224687453206</td>\n",
       "      <td>2919247920214845195</td>\n",
       "      <td>-6839713564940654454</td>\n",
       "      <td>-5932391056759866388</td>\n",
       "      <td>-6306904715218704629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6649324930261961840</td>\n",
       "      <td>2858911571784840089</td>\n",
       "      <td>3418451812342379591</td>\n",
       "      <td>3383016985780045156</td>\n",
       "      <td>2382911985227044227</td>\n",
       "      <td>-240041917918953337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4251899610700378615</td>\n",
       "      <td>2919247920214845195</td>\n",
       "      <td>-7358589937244777363</td>\n",
       "      <td>-7270992690764838239</td>\n",
       "      <td>-5265476641576484497</td>\n",
       "      <td>2816249700493135244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6213817087034420233</td>\n",
       "      <td>5804619920623030604</td>\n",
       "      <td>-8655945395761165989</td>\n",
       "      <td>-4966205278807386328</td>\n",
       "      <td>7476760589625268543</td>\n",
       "      <td>244937959911721367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-8930652370334418373</td>\n",
       "      <td>3972493657017129406</td>\n",
       "      <td>-8963554618409314978</td>\n",
       "      <td>-1115593437686158905</td>\n",
       "      <td>-5932391056759866388</td>\n",
       "      <td>-985918886318729368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               question                 tid0                  tid1  \\\n",
       "0   6215603645409872328  4610596224687453206   2919247920214845195   \n",
       "1   6649324930261961840  2858911571784840089   3418451812342379591   \n",
       "2  -4251899610700378615  2919247920214845195  -7358589937244777363   \n",
       "3   6213817087034420233  5804619920623030604  -8655945395761165989   \n",
       "4  -8930652370334418373  3972493657017129406  -8963554618409314978   \n",
       "\n",
       "                   tid2                  tid3                  tid4  \n",
       "0  -6839713564940654454  -5932391056759866388  -6306904715218704629  \n",
       "1   3383016985780045156   2382911985227044227   -240041917918953337  \n",
       "2  -7270992690764838239  -5265476641576484497   2816249700493135244  \n",
       "3  -4966205278807386328   7476760589625268543    244937959911721367  \n",
       "4  -1115593437686158905  -5932391056759866388   -985918886318729368  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_result.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>question</th>\n",
       "      <th>tid0</th>\n",
       "      <th>tid1</th>\n",
       "      <th>tid2</th>\n",
       "      <th>tid3</th>\n",
       "      <th>tid4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6215603645409872328</td>\n",
       "      <td>4610596224687453206</td>\n",
       "      <td>-6839713564940654454</td>\n",
       "      <td>-6306904715218704629</td>\n",
       "      <td>2919247920214845195</td>\n",
       "      <td>-8091907016971478715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6649324930261961840</td>\n",
       "      <td>3418451812342379591</td>\n",
       "      <td>2858911571784840089</td>\n",
       "      <td>2382911985227044227</td>\n",
       "      <td>-240041917918953337</td>\n",
       "      <td>3383016985780045156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-4251899610700378615</td>\n",
       "      <td>2919247920214845195</td>\n",
       "      <td>-7358589937244777363</td>\n",
       "      <td>2816249700493135244</td>\n",
       "      <td>-5265476641576484497</td>\n",
       "      <td>-3315241959305847628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6213817087034420233</td>\n",
       "      <td>-8655945395761165989</td>\n",
       "      <td>5804619920623030604</td>\n",
       "      <td>-4966205278807386328</td>\n",
       "      <td>7476760589625268543</td>\n",
       "      <td>-2523521411748733187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-8930652370334418373</td>\n",
       "      <td>3972493657017129406</td>\n",
       "      <td>-8963554618409314978</td>\n",
       "      <td>-1115593437686158905</td>\n",
       "      <td>1870872991887862017</td>\n",
       "      <td>6018641953300645757</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               question                  tid0                  tid1  \\\n",
       "0   6215603645409872328   4610596224687453206  -6839713564940654454   \n",
       "1   6649324930261961840   3418451812342379591   2858911571784840089   \n",
       "2  -4251899610700378615   2919247920214845195  -7358589937244777363   \n",
       "3   6213817087034420233  -8655945395761165989   5804619920623030604   \n",
       "4  -8930652370334418373   3972493657017129406  -8963554618409314978   \n",
       "\n",
       "                   tid2                  tid3                  tid4  \n",
       "0  -6306904715218704629   2919247920214845195  -8091907016971478715  \n",
       "1   2382911985227044227   -240041917918953337   3383016985780045156  \n",
       "2   2816249700493135244  -5265476641576484497  -3315241959305847628  \n",
       "3  -4966205278807386328   7476760589625268543  -2523521411748733187  \n",
       "4  -1115593437686158905   1870872991887862017   6018641953300645757  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 参考结果\n",
    "df_result.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 在全部预测正确的情况下，理论值为：f1=0.713933\n",
    "precision=2.50273, recall=0.998873, f1=0.713933"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "在全部预测正确的情况下，理论值为：\n",
      "precision=2.50273, recall=0.998873, f1=0.713933\n"
     ]
    }
   ],
   "source": [
    "# 假设全部正确，f1 值最高能到多少\n",
    "def padding_label(labels):\n",
    "    \"\"\"把所有的label补齐到长度为 5\"\"\"\n",
    "    label_len = len(labels)\n",
    "    if label_len >= 5:\n",
    "        return labels[:5]\n",
    "    return np.hstack([labels, np.zeros(5-label_len, dtype=int) - 1])\n",
    "    \n",
    "\n",
    "marked_labels_list = data_valid.y.tolist() # 所有的标注结果\n",
    "predict_labels_list = map(padding_label, marked_labels_list)\n",
    "predict_label_and_marked_label_list = zip(predict_labels_list, marked_labels_list)\n",
    "precision, recall, f1 = score_eval(predict_label_and_marked_label_list)\n",
    "print '在全部预测正确的情况下，理论值为：'\n",
    "print 'precision=%g, recall=%g, f1=%g' % (precision, recall, f1)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
